{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据说明\n",
    "\n",
    "1. Instant  记录号\n",
    "2. Dteday:日期\n",
    "3. Season:季节\n",
    " 1. 春天、\n",
    " 2. 夏天\n",
    " 3. 秋天\n",
    " 4. 冬天\n",
    "4. yr:年份，(0: 2011, 1:2012)\n",
    "5. mnth:月份( 1 to 12)\n",
    "6. hr:小时 (0 to 23) (只在 hour.csv 有，作业忽略此字段) holiday:是否是节假日\n",
    "7. weekday:星期中的哪天，取值为 0~6 workingday:是否工作日\n",
    "\t1. 1=工作日 (非周末和节假日)\n",
    "\t2. 0=周末\n",
    "8. weathersit:天气\n",
    "\t1. 晴天，多云\n",
    "\t2. 雾天，阴天\n",
    "\t3. 小雪，小雨\n",
    "\t4. 大雨，大雪，大雾\n",
    "9. temp:气温摄氏度\n",
    "10. atemp:体感温度\n",
    "11. hum:湿度\n",
    "12. windspeed:风速\n",
    "13. casual:非注册用户个数\n",
    "14. registered:注册用户个数\n",
    "15. cnt:给定日期(天)时间(每小时)总租车人数，响应变量 y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 训练数据和测试数据分割（请将2012年的数据作为测试数据）；（20分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654  \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670  \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229  \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454  \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "# %matplotlib inline\n",
    "\n",
    "# path to where the data lies\n",
    "#dpath = './data/'\n",
    "data = pd.read_csv(\"./Bike-Sharing-Dataset/day.csv\")\n",
    "\n",
    "# filter data by year\n",
    "# data_0 = data.query(\"yr==0\")\n",
    "# 根据年份进行训练和测试数据的分割\n",
    "data_0 = data[data.yr == 0]\n",
    "data_1 = data.query(\"yr==1\")\n",
    "\n",
    "# 提取不同的X 和 y\n",
    "y_train = data_0['cnt'].values\n",
    "y_test = data_1['cnt'].values\n",
    "X_train = data_0.drop('cnt', axis = 1)\n",
    "X_test = data_1.drop('cnt', axis = 1)\n",
    "\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 适当的特征工程（及数据探索）;（20分）\n",
    "\n",
    "1. 有些特征看起来是数据值特征，其实是类别型特征，如月份、季节\n",
    "2. 数值型特征归一化\n",
    "3. 可以丢弃一些不必要的特征"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 探索数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>2.496580</td>\n",
       "      <td>0.500684</td>\n",
       "      <td>6.519836</td>\n",
       "      <td>0.028728</td>\n",
       "      <td>2.997264</td>\n",
       "      <td>0.683995</td>\n",
       "      <td>1.395349</td>\n",
       "      <td>0.495385</td>\n",
       "      <td>0.474354</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.190486</td>\n",
       "      <td>848.176471</td>\n",
       "      <td>3656.172367</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>211.165812</td>\n",
       "      <td>1.110807</td>\n",
       "      <td>0.500342</td>\n",
       "      <td>3.451913</td>\n",
       "      <td>0.167155</td>\n",
       "      <td>2.004787</td>\n",
       "      <td>0.465233</td>\n",
       "      <td>0.544894</td>\n",
       "      <td>0.183051</td>\n",
       "      <td>0.162961</td>\n",
       "      <td>0.142429</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>686.622488</td>\n",
       "      <td>1560.256377</td>\n",
       "      <td>1937.211452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>183.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.337083</td>\n",
       "      <td>0.337842</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.134950</td>\n",
       "      <td>315.500000</td>\n",
       "      <td>2497.000000</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.486733</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>3662.000000</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>548.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.655417</td>\n",
       "      <td>0.608602</td>\n",
       "      <td>0.730209</td>\n",
       "      <td>0.233214</td>\n",
       "      <td>1096.000000</td>\n",
       "      <td>4776.500000</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3410.000000</td>\n",
       "      <td>6946.000000</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season          yr        mnth     holiday     weekday  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
       "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
       "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
       "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
       "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
       "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
       "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
       "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
       "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
       "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
       "\n",
       "            casual   registered          cnt  \n",
       "count   731.000000   731.000000   731.000000  \n",
       "mean    848.176471  3656.172367  4504.348837  \n",
       "std     686.622488  1560.256377  1937.211452  \n",
       "min       2.000000    20.000000    22.000000  \n",
       "25%     315.500000  2497.000000  3152.000000  \n",
       "50%     713.000000  3662.000000  4548.000000  \n",
       "75%    1096.000000  4776.500000  5956.000000  \n",
       "max    3410.000000  6946.000000  8714.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 单变量分布分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4U0CYmRkFk1GHM5Vt6LA5lC6HFMCAJ9VyuDz49GgtOmxOZKWE4865KQgxctCK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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117903b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.cnt.values, bins=30, kde=True)\n",
    "plt.xlabel('cnt', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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toHHo1QrB34PrM3ODSVT4sm05hdLguARvV4rY9DQot9st4xJ2DxcvSsvpk0pa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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b42f5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(data.shape[0]), data[\"cnt\"].values,color='purple')\n",
    "plt.title(\"Distribution of cnt\");\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 输入属性的直方图／分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uueewevVqODs7Y/z48UhMTLRG3f1K4Y1G5JY0oK6xDXlljTAYTQjxVSHUXwOVa7d/QroH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e612310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.season.values, bins=30, kde=True)\n",
    "plt.xlabel('season rate', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+AAAYo3wBADBG+QIAYIzyBQDAGOULAIAxyhcAAGOULwAAxihfAACMUb4AABijfAEAMEb5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e68e610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.mnth.values, bins=30, kde=False)\n",
    "plt.xlabel('mnth rate', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b36be10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.workingday.values, bins=4, kde=False)\n",
    "plt.xlabel('workingday rate', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wgCADAGAAQQYAwACCDACAAQQZAAADCDIAAAYQZAAADCDIAAAY4GvrBQAAcD28ufPIFT9n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\nAAAYQJABADCAIAMAYABBBgDAgP8CMRF3BRiPcu0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b377190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.weathersit.values, bins=30, kde=False)\n",
    "plt.xlabel('weathersit rate', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ea88550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.temp.values, bins=30, kde=False)\n",
    "plt.xlabel('temp rate', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 两两特征之间的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15, 15)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the names of all the columns\n",
    "cols=data_0.columns \n",
    "\n",
    "# Calculates pearson co-efficient for all combinations，通常认为相关系数大于0.5的为强相关\n",
    "data_corr = data_0.corr().abs()\n",
    "data_corr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CFlPz3Xf4bsiPWT6f15XzP2fPXyAqOpqPP2iaZY4XeX628dm20u+3ZLWaiauX8zAyggGt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\nIAil4L+w2AabBt8ZIQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ea9c3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)\n",
    "\n",
    "# Mask unimportant features\n",
    "sns.heatmap(data_corr, mask=data_corr < 1, cbar=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5 特征归一化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 日期是字符串，连续的，从 X 里删除\n",
    "2. 注册和新增人数转换成 float 类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331.0</td>\n",
       "      <td>654.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131.0</td>\n",
       "      <td>670.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1229.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1454.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82.0</td>\n",
       "      <td>1518.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant  season  yr  mnth  holiday  weekday  workingday  weathersit  \\\n",
       "0        1       1   0     1        0        6           0           2   \n",
       "1        2       1   0     1        0        0           0           2   \n",
       "2        3       1   0     1        0        1           1           1   \n",
       "3        4       1   0     1        0        2           1           1   \n",
       "4        5       1   0     1        0        3           1           1   \n",
       "\n",
       "       temp     atemp       hum  windspeed  casual  registered  \n",
       "0  0.344167  0.363625  0.805833   0.160446   331.0       654.0  \n",
       "1  0.363478  0.353739  0.696087   0.248539   131.0       670.0  \n",
       "2  0.196364  0.189405  0.437273   0.248309   120.0      1229.0  \n",
       "3  0.200000  0.212122  0.590435   0.160296   108.0      1454.0  \n",
       "4  0.226957  0.229270  0.436957   0.186900    82.0      1518.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# #  剔除掉日期的特征参数\n",
    "X_train2 = X_train.drop('dteday', axis = 1)\n",
    "X_test2 = X_test.drop('dteday', axis = 1)\n",
    "\n",
    "X_train2['casual'] =X_train2['casual'].astype(float)\n",
    "X_train2['registered'] =X_train2['registered'].astype(float)\n",
    "X_test2['casual'] =X_test2['casual'].astype(float)\n",
    "X_test2['registered'] =X_test2['registered'].astype(float)\n",
    "\n",
    "X_train2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wuzhong/anaconda3/envs/py27/lib/python2.7/site-packages/sklearn/utils/validation.py:429: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, _DataConversionWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[-1.72731195, -1.35081907,  0.        , ..., -0.40316146,\n",
       "        -0.6235799 , -1.95942453],\n",
       "       [-1.71782122, -1.35081907,  0.        , ...,  0.74411127,\n",
       "        -0.98361163, -1.94431104],\n",
       "       [-1.7083305 , -1.35081907,  0.        , ...,  0.74111588,\n",
       "        -1.00341338, -1.41628365],\n",
       "       ..., \n",
       "       [ 1.7083305 , -1.35081907,  0.        , ..., -0.93756476,\n",
       "        -0.76219212, -0.5283664 ],\n",
       "       [ 1.71782122, -1.35081907,  0.        , ..., -0.74319011,\n",
       "        -0.33555451, -0.20814944],\n",
       "       [ 1.72731195, -1.35081907,  0.        , ...,  0.3744413 ,\n",
       "        -0.0223269 , -0.8580293 ]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# 分别初始化对特征和目标值的标准化器\n",
    "ss_X = StandardScaler()\n",
    "ss_y = StandardScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征以及目标值进行标准化处理\n",
    "X_train_trans = ss_X.fit_transform(X_train2)\n",
    "X_test_trans = ss_X.transform(X_test2)\n",
    "\n",
    "#对y做标准化不是必须\n",
    "#对y标准化的好处是不同问题的w差异不太大，同时正则参数的范围也有限\n",
    "y_train_trans = ss_y.fit_transform(y_train.reshape(-1, 1))\n",
    "y_test_trans = ss_y.transform(y_test.reshape(-1, 1))\n",
    "\n",
    "X_train_trans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 岭回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of RidgeCV on test is 0.999994512395\n",
      "The r2 score of RidgeCV on train is 0.999999807937\n"
     ]
    }
   ],
   "source": [
    "#岭回归／L2正则\n",
    "#class sklearn.linear_model.RidgeCV(alphas=(0.1, 1.0, 10.0), fit_intercept=True, \n",
    "#                                  normalize=False, scoring=None, cv=None, gcv_mode=None, \n",
    "#                                  store_cv_values=False)\n",
    "from sklearn.linear_model import  RidgeCV\n",
    "\n",
    "#设置超参数（正则参数）范围\n",
    "alphas = [ 0.1, 1, 10]\n",
    "#n_alphas = 20\n",
    "#alphas = np.logspace(-5,2,n_alphas)\n",
    "\n",
    "#生成一个RidgeCV实例\n",
    "ridge = RidgeCV(alphas=alphas, store_cv_values=True)  \n",
    "\n",
    "#模型训练\n",
    "ridge.fit(X_train_trans, y_train_trans)    \n",
    "\n",
    "#预测\n",
    "y_test_pred_ridge = ridge.predict(X_test_trans)\n",
    "y_train_pred_ridge = ridge.predict(X_train_trans)\n",
    "\n",
    "\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print 'The r2 score of RidgeCV on test is', r2_score(y_test_trans, y_test_pred_ridge)\n",
    "print 'The r2 score of RidgeCV on train is', r2_score(y_train_trans, y_train_pred_ridge)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 参数调优\n",
    "2. 结果可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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77uOZZ57hq1/9Kh0dHSxevJghQ4ZQVFTEkiVLWL16NWPGjGHWrFn+KltERAaR0uquE/PM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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ecd2dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('alpha is:', 0.01)\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'columns' is not defined",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-18-6ee350c948f2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;31m# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mfs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"columns\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"coef_lr\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoef_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"coef_ridge\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mridge\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoef_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m \u001b[0mfs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'coef_lr'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mascending\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'columns' is not defined"
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "mse_mean = np.mean(ridge.cv_values_, axis = 0)\n",
    "plt.plot(np.log10(alphas), mse_mean.reshape(len(alphas),1)) \n",
    "\n",
    "#这是为了标出最佳参数的位置，不是必须\n",
    "#plt.plot(np.log10(ridge.alpha_)*np.ones(3), [0.28, 0.29, 0.30])\n",
    "\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()\n",
    "\n",
    "print ('alpha is:', ridge.alpha_)\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef_lr\":list((lr.coef_.T)), \"coef_ridge\":list((ridge.coef_.T))})\n",
    "fs.sort_values(by=['coef_lr'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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